We propose an unsupervised, source-free domain adaptation framework for 3D object detection that addresses the issue of incorrect, over-confident pseudo labels during self-training through the use of class prototypes. In the presence of label noise, standard feature aggregation methods of prototype computation [1]}, [2]}, [3]}, [4]} are insufficient, since features corresponding to incorrectly labeled regions could contribute to the final prototype (see Figure REF , top row). Inspired by the high representative power of self-attention and recent works that make use of transformers to focus on salient inputs [5]}, [6]}, we calculate an attentive class prototype by using a transformer to identify salient regions-of-interest and combine their associated feature vectors using prediction entropy weights that represent the uncertainty of the classification branch for each sample. Class predictions corresponding to incorrect pseudo labels, which are identified by calculating the similarity with the class prototype, are down-weighed to prevent reinforcing errors during self-training. We demonstrate our result on several domain shift scenarios (see Figure REF ). Our contributions are as follows
